why learning of graph representation? · representation learning on graphs 11/05/2019 1 hcmc, may...

Representation learning on graphs

11/05/2019 1

HCMC, May 2019

A/Prof Truyen TranDeakin University

truyentran.github.io

truyen.tran@deakin.edu.au @truyenoz

letdataspeak.blogspot.com

goo.gl/3jJ1O0

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Graph dynamics

Graph generation

Graph reasoning

Message passing

Embedding

Why bother?

Graph representation

Why learning of graph representation?

Graphs are pervasive in many scientific disciplines.

The sub-area of graph representation has reached a certain maturity, with multiple reviews, workshops and papers at top AI/ML venues.

Deep learning needs to move beyond vector, fixed-size data.

Learning representation as a powerful way to discover hidden patterns making learning, inference and planning easier.

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System medicine

11/05/2019 4https://www.frontiersin.org/articles/10.3389/fphys.2015.00225/full

Biology & pharmacy

Traditional techniques:

Graph kernels (ML)

Molecular fingerprints (Chemistry)

Modern techniques

Molecule as graph: atoms as nodes, chemical bonds as edges

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#REF: Penmatsa, Aravind, Kevin H. Wang, and Eric Gouaux. "X-ray structure of dopamine transporter elucidates antidepressant mechanism." Nature 503.7474 (2013): 85-90.

ChemistryDFT = Density Functional Theory

Gilmer, Justin, et al. "Neural message passing for quantum chemistry." arXiv preprint arXiv:1704.01212 (2017).

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• Molecular properties

• Chemical-chemical interaction

• Chemical reaction

• Synthesis planning

Materials science

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Xie, Tian, and Jeffrey C. Grossman. "Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties." Physical review letters 120.14 (2018): 145301.

• Crystal properties

• Exploring/generating solid structures

• Inverse design

Videos as space-time region graphs

(Abhinav Gupta et al, ECCV’18)

Knowledge graphs

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https://www.zdnet.com/article/salesforce-research-knowledge-graphs-and-machine-learning-to-power-einstein/

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Graph dynamics

Graph generation

Graph reasoning

Message passing

Embedding

Why bother?

Graph representation

Skip-gram

Negative sampling

Loss function

Predicting neighbors

DeepWalk (KDD-2014)

Considered as #epochs

For Hierarchical SoftmaxIterate over each epoch

Finding neighbours of each node

Update embedding of this node

Neighbour nodes Window size

Embedding matrix

Node2Vec (KDD-2016)Similar to DeepWalk in using Skip-gram model for unsupervised learning.

Only modifies the search for neighboring nodes that balance between BFS and DFS.

Defines edge embedding based on node embedding Can solve link prediction problem

2nd order Random WalkConsider random walk that just travelled edge (t, v). The walk will decide which is the next node x that it should go from vby computing

p and q are hyper-parameters

Node2Vec (Cont.)

Why it scales over DeepWalk?

Use Negative Sampling instead of Hierarchical Softmax

Batch learning

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Graph dynamics

Graph generation

Graph reasoning

Message passing

Embedding

Why bother?

Graph representation

Message passing

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Thin column

Relation graph Stacked learning

Column nets#REF: Pham, Trang, et al. "Column Networks for Collective Classification." AAAI. 2017.

Factored message passing

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#REF: Do, Kien, et al. "Attentional Multilabel Learning over Graphs-A message passing approach." Machine Learning, 2019.

Graph attention(Do et al arXiv’s17, Veličković et al ICLR’ 18)

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Learning deep matrix representations, K Do, T Tran, S Venkatesh, arXiv preprint arXiv:1703.01454

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Graph dynamics

Graph generation

Graph reasoning

Message passing

Embedding

Why bother?

Graph representation

Graph morphism

Input: Graph

Output: A new graph. Same nodes, different edges.

Model: Graph morphism

Method: Graph transformation policy network (GTPN)

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Kien Do, Truyen Tran, and Svetha Venkatesh. "Graph Transformation Policy Network for Chemical Reaction Prediction." KDD’19.

Graph recurrenceGraphs that represent interaction between entities through timeSpatial edges are node interaction at a time step

Temporal edges are consistency relationship through time

Challenges

The addition of temporal edges make the graphs bigger, more complex Relying on context specific constraints to reduce the

complexity by approximationsThrough time, structures of the graph may change Hard to solve, most methods model short sequences

to avoid this

Structural RNNCVPR16 best student paper Saxena group, Cornell

Example: human microwaving food Middle: s-t graph capturing spatial and temporal interactions between the human and the objects. Top: Schematic representation of structural-RNN architecture

Structural RNNNode features: human and object poses,

Edge features: relative orientation

Node labels: human activity and object affordance. affected by both its node and its interactions with other nodes (edges)

Structural RNN – from s-t graphEach factor is represented as a RNN

Form a feed forward bipartite graph from edge factors to node factors

Sharing factors between node and edge of same semantics type More compact Support node # changes

Structural RNN - ApplicationsActivity detection and anticipation

Skeleton trackingDriver maneuver prediction

Message-Passing Encoder-Decoder Recurrent Net

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• Prelim version published in (Morais et al, CVPR’19)

• Multiple interacting channels• Graphs are dynamics, with attention.• Mixture density network at each time step

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Graph dynamics

Graph generation

Graph reasoning

Message passing

Embedding

Why bother?

Graph representation

Technical challengesNo regular structures (e.g. grid, sequence,…)Graphs are permutation invariant: #permutations are exponential function of #nodesThe probability of a generated graph G need to be marginalized over all possible permutations

Generating graphs with variable sizeDiversity of generated graphsSmoothness of latent space

Generation methods

Classical random graph models, e.g., An exponential family of probability distributions for directed graphs (Holland and Leinhardt, 1981)

Deep Generative Model Methods: Variational Graph AutoEncoders Graphite: Iterative Generative Modeling of Graphs GraphVAE: Towards Generation of Small Graph using Variational AutoEncoder Junction Tree Variational AutoEncoder for Molecular Graph Generation

Sequence-based & RL method: GraphRNN - A Deep Generative Model for Graphs Multi-Objective De Novo Drug Design with Conditional Graph Generative Model

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Variational Autoencoder(Kingma & Welling, 2013)

Two separate processes: generative (hidden visible) versus recognition (visible hidden)

http://kvfrans.com/variational-autoencoders-explained/

Gaussian hidden variables

Data

Generative net

Recognisingnet

Generative Adversarial Networks(Goodfellow et al,2014)

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GAN architecture. Source: DL4J

(Goodfellow’s, NIPS 2014)

Variational methods

Minimize the upper bound on the negative log-likelihood, equivalent to maximizing the ELBO:

Adjacency matrix

Edge types Node types

k>nThe graph size are boundedLatent vector for

whole graph

GraphVAE (Simonovsky and Komodakis)

Junction Tree VAE (Jin et. al.)

Constraint during adding nodes

GraphRNN

A case of graph dynamics: nodes and edges are added sequentially.

Solve tractability using BFS

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You, Jiaxuan, et al. "GraphRNN: Generating realistic graphs with deep auto-regressive models." ICML (2018).

Graphs step-wise construction using reinforcement learning

11/05/2019 38You, Jiaxuan, et al. "Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation." NeurIPS (2018).

Graph rep (message passing) | graph validation (RL) | graph faithfulness (GAN)

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Graph dynamics

Graph generation

Graph reasoning

Message passing

Embedding

Why bother?

Graph representation

ReasoningReasoning is to deduce knowledge from previously acquired knowledge in response to a query (or a cue)Early theories of intelligence: focuses solely on reasoning, learning can be added separately and later! (Khardon & Roth, 1997).

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Khardon, Roni, and Dan Roth. "Learning to reason." Journal of the ACM (JACM) 44.5 (1997): 697-725.

(Dan Roth; ACM Fellow; IJCAI John McCarthy Award)

Inferring relations

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https://www.zdnet.com/article/salesforce-research-knowledge-graphs-and-machine-learning-to-power-einstein/

Do, Kien, Truyen Tran, and Svetha Venkatesh. "Knowledge graph embedding with multiple relation projections." 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018.

Querying a graph

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Controller

… Memory

Graph

Task/query Bioactivities

𝒚𝒚

query

#Ref: Pham, Trang, Truyen Tran, and Svetha Venkatesh. "Graph Memory Networks for Molecular Activity Prediction." ICPR’18.

Message passing as refining node & query representation

Querying a graph (2)

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https://rylanschaeffer.github.io/content/research/neural_turing_machine/main.html

Neural Turing Machine(Grave et al, 2014)

Differentiable Neural Computer (Grave et al, 2016)

Querying multiple graphs

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𝑴𝑴1 … 𝑴𝑴𝐶𝐶

𝒓𝒓𝑡𝑡1 …𝒓𝒓𝑡𝑡𝐾𝐾

𝒓𝒓𝑡𝑡∗

Controller

Write𝒉𝒉𝑡𝑡

Memory

Graph

Query Output

Read heads

Pham, Trang, Truyen Tran, and Svetha Venkatesh. "Relational dynamic memory networks." arXivpreprint arXiv:1808.04247(2018).

Detects high-order patterns from disconnected pathsLearns graph similarityModels graph-graph interactionSupports structured queries

On going and future workGraph translation, graph2graph, graph2seq

Graphs in context (e.g., crystals, human activities in scene)

Semi-supervised learning

Manifolds learning

Graph as hidden layers (e.g., see Ying et al, NIPS’19)

Generative models of sequence of graphs

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Theoretical properties of graphs (e.g., Xu et al, ICLR’19)

Graph matching

Higher-order graph neural networks (e.g., Morris, AAAI’19)

Graphs for logical inference

Graphs of multi-agent communication

Continuous time graph dynamics

Graph density – anomaly detection

For more refsWu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2019). A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596.

Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., ... & Gulcehre, C. (2018). Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261.

Goyal, P., & Ferrara, E. (2018). Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems, 151, 78-94.

Lee, J. B., Rossi, R. A., Kim, S., Ahmed, N. K., & Koh, E. (2018). Attention models in graphs: A survey. arXivpreprint arXiv:1807.07984.

Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4), 18-42.

Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs: Methods and applications. IEEE Data Engineering Bulletin.

Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE. 104.1, 11-33.

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The graph team @

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A/Prof. Truyen Tran

Dr Trang Pham(Now @Google)

Dr Vuong Le

Mr Kien DoMr Tin Pham Mr Thao Minh Le

Dr Thin Nguyen

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Thank you!

http://ahsanqawl.com/2015/10/qa/

We’re hiringPhD & Postdocs

truyen.tran@deakin.edu.auhttps://truyentran.github.io/scholarship.html